We’re now well from the stage that was AI buzz It’s becoming clear that the main issues with AI are centered around earning money instead of figuring ways to turn it into more useful. With the increase in AI experts and machine-learning services, AI is capable of providing massive value to many businesses. But when it comes to the implementation of AI firms typically fail to cover the initial investment. This is a bit strange, doesn’t it?
A recent IBM study has revealed the fact that just 21% of businesses are able to incorporate AI in their processes. That’s where the main problem is that it is impossible to get profits from the technology that’s never been implemented into production. Furthermore, even the AI projects that are implemented frequently don’t deliver the promised value.
Let’s talk about the obstacles businesses face when it comes to AI profits and ways to over come.
Get the workforce ready
Since AI is always heavily dependent on data and requires a data-driven culture, it is essential that the culture of the company adopting AI is driven by data. It’s not surprising that a lack of data culture is among the most common issues organizations overcome on their journey to fully realize the capabilities of AI.
If the leaders of the company and employees lack understanding of data, AI initiatives will most likely be unsuccessful. Even well-designed AI systems will not reach its full capabilities if employees don’t apply data-driven methods for decision-making. The lack of change management is another major omission when it comes to AI implementation.
Most of the time, AI calls for significant modifications to organizational structure and strategy, as also the employees’ attitudes and abilities. So, think about change management as a crucial component of your AI implementation plan and make sure that the leaders of your organization have the required expertise and motivation to build an AI-centric culture.
Set tangible goals
While goals are fundamental conditions for any project when it pertains to AI implementation, many businesses haven’t been able to identify their goals. It’s crucial to set precise expectations regarding the results from any AI initiative. Most of the time the end users aren’t involved actively in AI projects. Therefore, when the team of engineers creates perfect AI system, it’s likely to will provide very little value to the business. This is why it is crucial to engage all people involved from the beginning in the process.
Additionally, AI projects often bring benefits that are not quantifiable. For instance, increased satisfaction of employees or a better customer experience can be a lot more difficult to monitor than the cost or time savings. Let’s say, for instance, you create an AI system that reduces the time required by an IT department to classify tickets. In the first place, since the system must interpret free-form text with NLP however, it will not completely accurate particularly at the beginning. Your team will have to establish the acceptable error rate, and then account for this into your ROI calculation.
This is a different example Let’s suppose there’s an issue that is critical and requires immediate attention from IT personneland An AI-based system incorrectly determines that this issue is not a priority. This is a significant issue for ROI calculation because it’s not easy to quantify the negative effects in such a situation.
This is the reason it’s crucial to begin with projects that have ROI expectations can be correctly estimated. For instance manufacturers have had success in generating economic returns from AI initiatives to control quality, since their ROI is rather simple to gauge.
Start with a small
While it’s tempting for companies to develop massive AI systems, choosing the low-hanging fruits is usually the most effective option particularly at the beginning. It’s an ideal idea to begin using robotic process automatization (RPA) is a method that tends to be less expensive than AI and offers a relatively quick ROI. RPA implementation isn’t intrusive and doesn’t alter the flow of older systems as many AI solutions do.
AI projects that prove to be fast wins could be used to justify greater AI investments, and also ensure that stakeholders are involved in the future.
AI requires maturity
Although it might sound like a superficial, companies which have more experience and are mature are more likely to be successful in benefiting from AI. They typically be well-established in their data management processes as well as elaborate training programs performance tracking systems, and clearly defined objectives for projects. These are the key differences between firms that are successful in AI implementation versus those that aren’t.
With the fluctuation of success rates for projects, AI calls for a solid foundation in management areas, more so than other technology. The extent to which businesses are able to monitor, evaluate and manage their processes is correlated with their likelihood of benefiting from AI.